Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models

Santiago Cepeda, Roberto Romero, Daniel Garcia-Perez, Guillermo Blasco, Luigi Tommaso Luppino +9 more
4/17/2024
eess.IVcs.CV

No official code found. Best implementation: smcch/Postoperative-Glioblastoma-Segmentation (13 stars, last commit 2025-04-17).

Abstract

Accurately assessing tumor removal is paramount in the management of glioblastoma. We developed a pipeline using MRI scans and neural networks to segment tumor subregions and the surgical cavity in postoperative images. Our model excels in accurately classifying the extent of resection, offering a valuable tool for clinicians in assessing treatment effectiveness.

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Code Implementations(1)

smcch/Postoperative-Glioblastoma-SegmentationCommunity100%
131PythonMar 5, 20241 years agoNOASSERTION

Cite this paper

@article{cepeda2024postoperative,
  title  = {Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models},
  author = {Santiago Cepeda and Roberto Romero and Daniel Garcia-Perez and Guillermo Blasco and Luigi Tommaso Luppino and Samuel Kuttner and Ignacio Arrese and Ole Solheim and Live Eikenes and Anna Karlberg and Angel Perez-Nunez and Trinidad Escudero and Roberto Hornero and Rosario Sarabia},
  year   = {2024},
  eprint = {2404.11725},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.11725v1}
}

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